A scalable cloud-based framework for multi-modal mapping across single neuron omics, morphology and electrophysiology

项目摘要 将单个神经元分类为不同的组或细胞类型，是研究神经的经典方法 系统。随着越来越多的工具被发明来观察神经元，新的标准被创造出来 描述细胞的不同方面或形态。虽然这些特定于医疗模式的分类具有 凭借对神经科学的深入了解，不同标准之间的不一致导致了数据集成 跨医疗模式在技术上很困难。此外，使用不同的分类方法的“相似细胞”的不一致 标准导致将神经科学界划分为以形态为中心的亚组。要解决这个问题 问题是，必须建立客观的方法来定义包含多种模式的细胞相似性 并以一种开放、可接触和吸引广大神经科学界的方式进行开发。我们 建议开发一个可广泛访问的基于云的框架，以实现集成的、多模式的脑细胞 Atlas使用新颖、可扩展的分析工具，利用联合的Brain Initiative资源和社区 订婚。自我监督的学习方法纯粹是由数据驱动的，并允许高度准确 基于单个或多个测量模式的相似细胞的识别，而不假定 特定于医疗设备的课程。它还支持跨通道映射，其中未观察到的测量可以 从单模式数据推断，在低模式下实现脑细胞映射工作的计算“放大” 吞吐方式，如电生理学或形态学。随着更多多模式数据的收集和 加上这项研究，这种推断的算法准确性将继续以自动化的方式增长。这个 开源方法将被产品化到云本地管道中，用于单个医疗设备和 跨模式映射，并作为生态系统的一部分安装在云计算工作台中， 开放数据分析。该云生态系统将演示对Brain Initiative托管的数据存储库的访问 分子(NEMO)、神经生理学(DANDI)和显微镜(BIL)数据，因此来自不同的数据集 可以将信号源放入一个公共工作区进行综合分析。此外，云生态系统 将提供一个用户友好的数据门户，用于可视化和导航来自 知识库，以及浏览细胞相似度查询结果。这个生态系统将支持公平原则 并促进协作研究并寻求与数据存储库的扩展集成。通博通 与神经科学社区的接触和接触，该项目将提供资源，以建立 集成的脑细胞图谱，便于对大脑进行多模式表征。

Project Summary Categorizing individual neurons into different groups, or cell types, is a classical approach to studying the nervous system. With increasingly more tools being invented to observe the neurons, new criteria were created to characterize different aspects, or modalities, of the cells. While these modality-specific categorizations have enabled in-depth knowledge in neuroscience, the inconsistencies across different criteria leave data integration across modalities technically difficult. In addition, disagreements of “similar cells” using different categorization criteria have resulted in division of the neuroscience community into modality-centric subgroups. To solve this problem, objective approaches to define cell similarities incorporating multiple modalities must be established and developed in a way that is open, accessible, and engaging to the neuroscience community at large. We propose to develop a broadly accessible cloud-based framework toward an integrative, multi-modal brain cell atlas using novel, scalable analytics tools, leveraging federated BRAIN Initiative resources and community engagement. The self-supervised learning methodology is purely data-driven, and allows highly accurate identification of similar cells based on single or multiple modalities of measurements, without presumption about modality-specific classes. It also enables cross-modality mapping, where unobserved measurements can be inferred from single-modal data, achieving computational “scale-up” of the brain cell mapping efforts in low- throughput modalities such as electrophysiology or morphology. With more multimodal data being collected and added to the study, the algorithm accuracy of this inference will continue to grow in an automated way. The open-source methodology will be productionized into cloud-native pipelines for individual modalities and for cross-modality mapping, and installed in a cloud computing workbench as a part of the ecosystem, for scalable, open data analyses. This cloud ecosystem will demonstrate access to BRAIN Initiative data repositories hosting molecular (NeMO), neurophysiology (DANDI), and microscopy (BIL) data, such that datasets from different sources can be brought into a common workspace for integrative analyses. Furthermore, the cloud ecosystem will provide a user-friendly data portal for visualization and navigation of the multi-modal single cell data from the repositories, as well as exploring the cell similarity query results. This ecosystem will support FAIR principles and promote collaborative research and seek for extended integrations with data repositories. Through broad engagement and outreach to neuroscience communities, this project will provide resources for building an integrated brain cell atlas and facilitate the multimodal characterization of the brain.